update

Author: mrbullwinkle

articles/ai-services/openai/how-to/batch-blob-storage.md

@@ -105,7 +105,7 @@ Select **Upload** and select your `test.jsonl` file.
 
 :::image type="content" source="../media/how-to/batch-blob-storage/upload.png" alt-text="Screenshot that shows Azure Storage Blob container upload UX." lightbox="../media/how-to/batch-blob-storage/upload.png":::
 
-During the time that we are processing your `jsonl` file as part of the batch job, you cannot make any changes to the file. If a file changes while the batch job is running the job will fail.
+During the time that we're processing your `jsonl` file as part of the batch job, you can't make any changes to the file. If a file changes while the batch job is running the job will fail.
 
 ## Create batch job
 
@@ -274,7 +274,7 @@ print(batch_response.model_dump_json(indent=2))
 Once your batch job is complete, you can download the `error_blob` and `output_blob` via the Azure Blob Storage interface in the Azure portal or you can download programmatically:
 
 > [!NOTE]
-> `error_blob`, and `output_blob` paths are always returned in the response even in cases where a corresponding file is not created. In this case there were no errors so `errors.jsonl` was not created, only `results.jsonl` exists.
+> `error_blob`, and `output_blob` paths are always returned in the response even in cases where a corresponding file isn't created. In this case there were no errors so `errors.jsonl` wasn't created, only `results.jsonl` exists.
 
 ```cmd
 pip install azure-identity azure-storage-blob

articles/ai-services/openai/how-to/batch.md

@@ -36,10 +36,10 @@ Key use cases include:
 > [!TIP]
 > If your batch jobs are so large that you are hitting the enqueued token limit even after maxing out the quota for your deployment, certain regions now support a new feature that allows you to queue multiple batch jobs with exponential backoff. 
 >
->Once your enqueued token quota is available, the next batch job can be created and kicked off automatically.To learn more, see [**automating retries of large batch jobs with exponential backoff**](#queueing-batch-jobs).
+>Once your enqueued token quota is available, the next batch job can be created and kicked off automatically. To learn more, see [**automating retries of large batch jobs with exponential backoff**](#queueing-batch-jobs).
 
 > [!IMPORTANT]
-> We aim to process batch requests within 24 hours; we don't expire the jobs that take longer. You can [cancel](#cancel-batch) the job anytime. When you cancel the job, any remaining work is cancelled and any already completed work is returned. You'll be charged for any completed work.
+> We aim to process batch requests within 24 hours; we don't expire the jobs that take longer. You can [cancel](#cancel-batch) the job anytime. When you cancel the job, any remaining work is canceled and any already completed work is returned. You'll be charged for any completed work.
 >
 > Data stored at rest remains in the designated Azure geography, while data may be processed for inferencing in any Azure OpenAI location. [Learn more about data residency](https://azure.microsoft.com/explore/global-infrastructure/data-residency/).  
 
@@ -95,7 +95,7 @@ The following aren't currently supported:
 :::image type="content" source="../media/how-to/global-batch/global-batch.png" alt-text="Screenshot that shows the model deployment dialog in Azure AI Foundry portal with Global-Batch deployment type highlighted." lightbox="../media/how-to/global-batch/global-batch.png":::
 
 > [!TIP]
-> We recommend enabling **dynamic quota** for all global batch model deployments to help avoid job failures due to insufficient enqueued token quota. Dynamic quota allows your deployment to opportunistically take advantage of more quota when extra capacity is available. When dynamic quota is set to off, your deployment will only be able to process requests up to the enqueued token limit that was defined when you created the deployment.
+> We recommend enabling **dynamic quota** for all global batch model deployments to help avoid job failures due to insufficient enqueued token quota. Using dynamic quota allows your deployment to opportunistically take advantage of more quota when extra capacity is available. When dynamic quota is set to off, your deployment will only be able to process requests up to the enqueued token limit that was defined when you created the deployment.
 
 ::: zone pivot="ai-foundry-portal"